1. Technical Field
The present invention relates to multi-modal image registration, and more particularly to a system and method for non-rigidly registering multi-modal image data using statistical learning methods.
2. Discussion of Related Art
Non-rigid multi-modal image/volume registration is an open research problem and an important part of ongoing research. Non-rigid multi-modal image registration in medical applications has become significantly important to doctors. In such an environment, accuracy, robustness, and performance are needed to reliably support diagnoses. Current non-rigid multi-modal registration technology is increasingly sophisticated but context-free.
The fusion of complimentary image information has been shown to be particularly beneficial to diagnosis. Furthermore, imaging techniques such as molecular imaging need multi-modal image registration to display functional, anatomical and/or molecular image information in a single fused image.
An important challenge of non-rigid multi-modal registration is handling unresolved correspondences of image/volume locations that arise from attempts to associate functional areas with anatomy. It is an open problem to retrieve those correspondences reliably and in a way that is meaningful to the user, e.g., doctor. Existing non-rigid image registration methods address unresolved correspondences by stating an energy functional which global optimum presents a solution to the underlying registration problem. Such an energy functional may, for example, be composed of an attraction potential, e.g., similarity/distance measure, and a regularizing term that constrains the potential in a somewhat meaningful way. Regularization is needed due to the ill-posed nature of the registration problem. A proposed solution may be found by either walking along a gradient or solving a PDE associated with the problem. The ill posedness of the energy functional and/or the size of the parameter space are still problematic while retrieving a global optimum by using regularization. Regularization may lead to mis-registration or convergence to a local extremum.
Therefore, a need exists for a system and method for non-rigidly registering multi-modal image-data using statistical learning methods.